Pulmonary nodule intelligent diagnosis method based on mixed characteristics

Abstract

The invention discloses a pulmonary nodule intelligent diagnosis method based on mixed characteristics, and the method comprises the steps: obtaining CT image data which comprises a chest medical image file and a corresponding diagnosis result lesion mark; carrying out resampling, smoothing processing and normalization processing on the acquired CT image data; learning a 3D CT image by using a 3Dresidual-tight connection network to obtain high-dimensional depth features, and obtaining LBP-based texture features and HOG-based shape features for describing characterization features of pulmonarynodules; and classifying benign and malignant pulmonary nodules by adopting a GBM gradient elevator based on the high-dimensional depth features, the LBP-based texture features and the HOG-based shape features. According to the method, benign and malignant pulmonary nodules can be quickly distinguished in CT medical images, the misjudgment rate is reduced, the classification precision is improved, and the generalization ability of a diagnosis network model is enhanced.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to an intelligent diagnosis method for pulmonary nodules based on mixed features. Background technique [0002] Traditional pulmonary nodule automatic diagnosis algorithms include five stages: CT image acquisition, pulmonary nodule segmentation, prior feature extraction, feature screening, and classification of benign and malignant nodules. The prior feature extraction of pulmonary nodules is a crucial step. Currently the most commonly used features include texture features, shape (geometric) features, grayscale features, size features, contour features and morphological features. After selecting valid features, classify them using machine learning-based classifiers. However, due to the heterogeneity of the characteristics of pulmonary nodules, and the complex shape and various types of pulmonary nodules, the extracted prior features cannot accurately describe the...

AI Technical Summary

Problems solved by technology

[0004] However, the existing convolutional neural classification network under deep learning is mainly based on 2DCT images to process lung nodule slices one by one, largely ignoring the three-dimensional spatial information of CT images, and the learned feature dimension is single, and its discrimination ability is still low. limited

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